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THE AGENSI STORE
71 skills found
An adversarial reviewer for AGENTS.md and agent instruction files. It flags ambiguous or contradictory rules, missing guardrails, vague tool and scope definitions, and untestable instructions, then returns a PASS / REVISE / BLOCK verdict — before the config drives your agent.
Map the blast radius of a code change before you run the whole suite. For the files and functions you changed, it lists what imports or calls them, which tests cover them, flags any change with no covering test, and warns when a file has a lot of dependents. It tells an agent what its edit might break instead of making it guess. Resolves Python and JavaScript/TypeScript.
Find the unit tests that pass without testing anything. Flags tests with no assertions, trivial existence-only checks (toBeDefined, assertIsNotNone), tests that assert the exact value they just mocked, snapshot-only tests, tautological assertions (expect(true).toBe(true)), empty placeholders, and over-mocked tests with more setup than assertions. Works on Jest/Vitest and pytest/unittest.
Generate runnable accessibility regression tests, not just a findings report. Detects a11y issues, missing alt text, unlabeled controls, keyboard and focus gaps, in your routes, components, or HTML, then emits Playwright + axe-core spec files with targeted assertions and remediation tickets for each. Previews the tests first and writes them only on your confirmation.
One-line summary description Stop your agent from claiming "done" before it's proven. A verification gate that classifies each change by risk (payment, auth, database, user-facing), picks the tests that actually cover it, demands evidence, maps regression risk, and outputs an honest pass/fail report. Turns "looks good to me" into "here's what I ran, and here's what's still unverified."
Audit your dbt project for the test and documentation gaps that let bad data ship. Flags models with no unique or not_null tests, sources missing freshness config or tests, likely keys without a not_null test, models missing descriptions, SELECT * in models, and raw table references that should use ref() or source(). Each finding comes with a suggested tests: YAML snippet to drop into schema.yml.
Diagnose why Stripe Checkout, the Billing Portal, or your webhooks aren't working: missing signature verification, raw-body mistakes, no idempotency, test/live key mix-ups, broken success and cancel URLs, and subscription lifecycle gaps. Works across Next.js, Express, FastAPI, and serverless, with severity, evidence, and copy-paste fixes for each finding.
Lint an OpenAPI spec and diff two versions to catch breaking API changes before they reach consumers. Within a spec it flags missing operationId, success responses with no schema, untyped parameters, operations with no security requirement, and unbounded arrays/strings. Across two versions it flags removed paths and operations, removed response codes, a parameter becoming required, narrowed parameter types, removed enum values, and removed response fields. Analyzes JSON specs (convert YAML to JSON first).
Generate a personalized before-you-ship checklist for your app, with the things you already have checked off. Pick your app type (web app, SaaS, store, or content) and it builds a tailored list covering security, legal basics (privacy policy, terms, cookie consent), social preview, payments and refunds, accessibility, analytics, and backups, then scans your repo and pre-checks what is already there. Every item says why it matters in plain English.
by Shogun Labs
Battle-tested prompting patterns to eliminate LLM output drift. Sandwich structure, few-shot examples, history limits, retry, and token caps — 6 composable layers for production-grade agent reliability.
Audit your frontend build against a performance budget and catch size regressions before you ship. Flags total bundle over budget, initial bundle over budget, individual chunks over a threshold, oversized image assets, source maps shipped to production, and large unminified JavaScript. Reads a webpack or Vite-style stats.json plus a perf-budget.json you control.
by Joker
Product management across 7 product types. RICE prioritization, PRD frameworks, roadmap planning, A/B testing, data-driven decisions.
Run structural QA on your translation files across locales. Flags missing keys, placeholder mismatches ({name}, %s, {{var}}), strings left untranslated and identical to the source, length-overflow risk that breaks UI, terminology drift against a glossary, empty targets, and plural-category gaps. Works on JSON, gettext .po/.pot, and .properties. It checks form, not meaning, so you do not need to speak the target language to use it.
Deploy battle-tested SRE workflows, blameless postmortems, and deployment checklists for high-reliability teams.
Generate a shareable stat card for any repo, with an honest health grade. It reads the file tree and renders a card showing the language mix, file count and size, whether tests, docs, and a license are present, and an A-to-F health grade from a documented rubric. Markdown by default, SVG with a flag. The kind of card people actually post.
'Should work now' is the most expensive sentence in software — it means the change was reasoned about, not observed, and the next person to touch this code will pay for that assumption
by Nex AI
Stop underpricing your Belgian agency work with sector-specific 3-tier retainer pricing and market-tested anchors.
Write user stories with real acceptance criteria — not tautologies, not implementation instructions, but observable outcomes engineers can test.
by Ifásola
Stop guessing and start proving: Force your AI agent to provide verifiable execution logs for every 'done' claim.
Generate a real test suite for any function, module, or file — meaningful edge cases, error paths, boundary conditions, and proper mocks, not happy-path stubs. Detects your project's framework and conventions, plans the cases deliberately before writing, and hands back runnable tests plus a summary of what's covered. Built to write the tests that actually catch bugs.
Stop leaving your AI startup exposed to malicious users trying to steal your proprietary system prompts or bypass your paywalls. The AI Prompt Injection Defense Shield is an automated code review agent that deeply analyzes your Next.js or Python backend, instantly detecting insecure LLM input fields, un-sanitized API data streams, and weak prompt boundaries. By automatically generating the exact copy-paste code patches required to harden your AI wrapper against the latest OWASP top 10 LLM vulnerabilities, this skill allows solo developers and indie hackers to confidently launch their SaaS without the fear of massive, unexpected API billing spikes or catastrophic data leaks.
by Shippers
Convert a plain-English user flow plus a route/component map into a runnable Playwright, Cypress, or Detox test file wit
by Shandra
Audits worksheets and activities for ages 3-6, checking age fit, clarity, difficulty, safety, vocabulary, instructions, visual load, learning objective alignment, confusion risks, and marketplace readiness.
by Roy Yuen
Detect and analyze flaky tests across multiple frameworks with automated repeated execution and severity reporting.